Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f7eb5c3f390>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f7eb5b67f60>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    """
    Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
    Paper: https://arxiv.org/abs/1511.06434
    Example: https://github.com/carpedm20/DCGAN-tensorflow
    """
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Hidden layer
        h1 = tf.layers.conv2d(images, 64, 5, 2, 'same')
        # Leaky ReLU
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = tf.layers.conv2d(h1, 128, 5, 2, 'same')
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        
        h3 = tf.layers.conv2d(h2, 256, 5, 1, 'same')
        h3 = tf.layers.batch_normalization(h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        
        h4 = tf.layers.conv2d(h3, 512, 5, 1, 'same')
        h4 = tf.layers.batch_normalization(h4, training=True)
        h4 = tf.maximum(alpha * h4, h4)
        
        flat = tf.reshape(h4, (-1, 7*7*512))
        
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        
        h1 = tf.layers.dense(z, 7*7*512)
        h1 = tf.reshape(h1, (-1, 7, 7, 512))
        h1 = tf.maximum(alpha * h1, h1)
    
        h2 = tf.layers.conv2d_transpose(h1, 256, 3, 1, 'same')
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
    
        h3 = tf.layers.conv2d_transpose(h2, 128, 3, 1, 'same')
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
        
        h4 = tf.layers.conv2d_transpose(h3, 64, 3, 2, 'same')
        h4 = tf.layers.batch_normalization(h4, training=is_train)
        h4 = tf.maximum(alpha * h4, h4)
    
        # Logits and tanh output
        logits = tf.layers.conv2d_transpose(h4, out_channel_dim, 3, 2, 'same')
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    smooth = 0.1
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits
                                 (logits=d_logits_real, 
                                  labels=tf.ones_like(d_model_real) * (1 - smooth)))

    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits
                                 (logits=d_logits_fake, 
                                  labels=tf.zeros_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits
                            (logits=d_logits_fake, 
                             labels=tf.ones_like(d_model_fake)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get the trainable_variables, split into G and D parts
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]

    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    input_real, input_z, _ = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    step_count = 0
    step_print = 10
    step_example = 100
    losses = []
    show_n_images = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                # Get images
                step_count += 1
                batch_images = batch_images * 2
                
                 # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z})
                
                if step_count % step_print == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                        "Discriminator Loss: {:.4f}...".format(train_loss_d),
                        "Generator Loss: {:.4f}".format(train_loss_g))    
                    
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                    
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.7918... Generator Loss: 1.4460
Epoch 1/2... Discriminator Loss: 1.0742... Generator Loss: 5.1640
Epoch 1/2... Discriminator Loss: 1.5360... Generator Loss: 1.1884
Epoch 1/2... Discriminator Loss: 1.4059... Generator Loss: 4.1031
Epoch 1/2... Discriminator Loss: 2.1060... Generator Loss: 0.2817
Epoch 1/2... Discriminator Loss: 2.5123... Generator Loss: 0.2938
Epoch 1/2... Discriminator Loss: 1.2188... Generator Loss: 1.0310
Epoch 1/2... Discriminator Loss: 1.6315... Generator Loss: 0.4373
Epoch 1/2... Discriminator Loss: 2.7093... Generator Loss: 4.8857
Epoch 1/2... Discriminator Loss: 1.5735... Generator Loss: 0.6318
Epoch 1/2... Discriminator Loss: 3.9281... Generator Loss: 0.0699
Epoch 1/2... Discriminator Loss: 2.1394... Generator Loss: 0.2811
Epoch 1/2... Discriminator Loss: 3.6242... Generator Loss: 3.3381
Epoch 1/2... Discriminator Loss: 2.0617... Generator Loss: 0.2572
Epoch 1/2... Discriminator Loss: 2.8417... Generator Loss: 2.9649
Epoch 1/2... Discriminator Loss: 1.1860... Generator Loss: 1.0436
Epoch 1/2... Discriminator Loss: 1.3682... Generator Loss: 1.0957
Epoch 1/2... Discriminator Loss: 1.5196... Generator Loss: 1.5804
Epoch 1/2... Discriminator Loss: 1.6167... Generator Loss: 1.5651
Epoch 1/2... Discriminator Loss: 1.1400... Generator Loss: 1.2507
Epoch 1/2... Discriminator Loss: 2.9093... Generator Loss: 1.5289
Epoch 1/2... Discriminator Loss: 3.1946... Generator Loss: 3.0629
Epoch 1/2... Discriminator Loss: 2.1142... Generator Loss: 0.2466
Epoch 1/2... Discriminator Loss: 2.1717... Generator Loss: 1.0973
Epoch 1/2... Discriminator Loss: 1.5446... Generator Loss: 0.8616
Epoch 1/2... Discriminator Loss: 1.0737... Generator Loss: 1.1575
Epoch 1/2... Discriminator Loss: 3.0786... Generator Loss: 0.1452
Epoch 1/2... Discriminator Loss: 1.7431... Generator Loss: 1.4870
Epoch 1/2... Discriminator Loss: 1.0969... Generator Loss: 2.0437
Epoch 1/2... Discriminator Loss: 2.8201... Generator Loss: 0.1264
Epoch 1/2... Discriminator Loss: 2.0901... Generator Loss: 1.9883
Epoch 1/2... Discriminator Loss: 1.3911... Generator Loss: 0.6252
Epoch 1/2... Discriminator Loss: 1.7725... Generator Loss: 1.5235
Epoch 1/2... Discriminator Loss: 1.1043... Generator Loss: 1.6201
Epoch 1/2... Discriminator Loss: 1.7583... Generator Loss: 1.1044
Epoch 1/2... Discriminator Loss: 1.8250... Generator Loss: 0.4590
Epoch 1/2... Discriminator Loss: 1.7129... Generator Loss: 1.3271
Epoch 1/2... Discriminator Loss: 1.3466... Generator Loss: 0.6852
Epoch 1/2... Discriminator Loss: 1.9848... Generator Loss: 2.6446
Epoch 1/2... Discriminator Loss: 1.8466... Generator Loss: 0.4204
Epoch 1/2... Discriminator Loss: 1.4957... Generator Loss: 1.4926
Epoch 1/2... Discriminator Loss: 2.2165... Generator Loss: 0.2201
Epoch 1/2... Discriminator Loss: 1.6810... Generator Loss: 0.6540
Epoch 1/2... Discriminator Loss: 1.5182... Generator Loss: 0.7647
Epoch 1/2... Discriminator Loss: 1.6397... Generator Loss: 2.0445
Epoch 1/2... Discriminator Loss: 1.3694... Generator Loss: 1.0860
Epoch 1/2... Discriminator Loss: 0.8810... Generator Loss: 1.1201
Epoch 1/2... Discriminator Loss: 2.9304... Generator Loss: 0.0942
Epoch 1/2... Discriminator Loss: 1.9402... Generator Loss: 0.7733
Epoch 1/2... Discriminator Loss: 1.5356... Generator Loss: 0.5897
Epoch 1/2... Discriminator Loss: 1.1099... Generator Loss: 0.7135
Epoch 1/2... Discriminator Loss: 2.3628... Generator Loss: 1.9602
Epoch 1/2... Discriminator Loss: 1.1176... Generator Loss: 1.0838
Epoch 1/2... Discriminator Loss: 1.6395... Generator Loss: 2.4407
Epoch 1/2... Discriminator Loss: 2.2583... Generator Loss: 0.3637
Epoch 1/2... Discriminator Loss: 3.1760... Generator Loss: 0.1294
Epoch 1/2... Discriminator Loss: 2.6516... Generator Loss: 0.2870
Epoch 1/2... Discriminator Loss: 1.9898... Generator Loss: 1.0136
Epoch 1/2... Discriminator Loss: 1.6568... Generator Loss: 3.0218
Epoch 1/2... Discriminator Loss: 2.6517... Generator Loss: 0.1677
Epoch 1/2... Discriminator Loss: 1.9972... Generator Loss: 2.4180
Epoch 1/2... Discriminator Loss: 1.1043... Generator Loss: 0.8527
Epoch 1/2... Discriminator Loss: 1.8134... Generator Loss: 0.8551
Epoch 1/2... Discriminator Loss: 1.0303... Generator Loss: 1.3487
Epoch 1/2... Discriminator Loss: 1.2019... Generator Loss: 1.4245
Epoch 1/2... Discriminator Loss: 1.6262... Generator Loss: 0.4363
Epoch 1/2... Discriminator Loss: 2.0344... Generator Loss: 3.2308
Epoch 1/2... Discriminator Loss: 0.9194... Generator Loss: 1.5355
Epoch 1/2... Discriminator Loss: 1.0551... Generator Loss: 1.0267
Epoch 1/2... Discriminator Loss: 1.0393... Generator Loss: 2.1487
Epoch 1/2... Discriminator Loss: 1.1483... Generator Loss: 1.2346
Epoch 1/2... Discriminator Loss: 1.1758... Generator Loss: 1.7474
Epoch 1/2... Discriminator Loss: 1.2386... Generator Loss: 0.7255
Epoch 1/2... Discriminator Loss: 2.4817... Generator Loss: 3.1268
Epoch 1/2... Discriminator Loss: 1.4112... Generator Loss: 1.7744
Epoch 1/2... Discriminator Loss: 0.9946... Generator Loss: 1.1082
Epoch 1/2... Discriminator Loss: 2.2750... Generator Loss: 0.2674
Epoch 1/2... Discriminator Loss: 1.3011... Generator Loss: 1.8701
Epoch 1/2... Discriminator Loss: 2.2305... Generator Loss: 0.3313
Epoch 1/2... Discriminator Loss: 2.2834... Generator Loss: 0.4886
Epoch 1/2... Discriminator Loss: 1.6387... Generator Loss: 0.5717
Epoch 1/2... Discriminator Loss: 1.0135... Generator Loss: 1.2257
Epoch 1/2... Discriminator Loss: 1.5922... Generator Loss: 0.5683
Epoch 1/2... Discriminator Loss: 1.0763... Generator Loss: 1.5957
Epoch 1/2... Discriminator Loss: 2.3021... Generator Loss: 3.1985
Epoch 1/2... Discriminator Loss: 1.4153... Generator Loss: 0.6872
Epoch 1/2... Discriminator Loss: 1.2457... Generator Loss: 0.8040
Epoch 1/2... Discriminator Loss: 1.5252... Generator Loss: 0.5500
Epoch 1/2... Discriminator Loss: 2.2734... Generator Loss: 0.2512
Epoch 1/2... Discriminator Loss: 1.3210... Generator Loss: 0.7455
Epoch 1/2... Discriminator Loss: 1.0440... Generator Loss: 1.3890
Epoch 1/2... Discriminator Loss: 1.2386... Generator Loss: 0.7815
Epoch 1/2... Discriminator Loss: 1.8676... Generator Loss: 1.9146
Epoch 1/2... Discriminator Loss: 0.9228... Generator Loss: 1.8328
Epoch 1/2... Discriminator Loss: 1.1888... Generator Loss: 1.3283
Epoch 1/2... Discriminator Loss: 1.9621... Generator Loss: 0.3479
Epoch 1/2... Discriminator Loss: 1.0647... Generator Loss: 0.9173
Epoch 1/2... Discriminator Loss: 0.9487... Generator Loss: 1.7998
Epoch 1/2... Discriminator Loss: 1.1398... Generator Loss: 1.2337
Epoch 1/2... Discriminator Loss: 1.1403... Generator Loss: 0.9138
Epoch 1/2... Discriminator Loss: 0.9719... Generator Loss: 1.8298
Epoch 1/2... Discriminator Loss: 0.9669... Generator Loss: 1.6115
Epoch 1/2... Discriminator Loss: 1.5588... Generator Loss: 0.7185
Epoch 1/2... Discriminator Loss: 0.9821... Generator Loss: 1.3266
Epoch 1/2... Discriminator Loss: 2.4158... Generator Loss: 0.3203
Epoch 1/2... Discriminator Loss: 1.3375... Generator Loss: 1.5849
Epoch 1/2... Discriminator Loss: 1.1322... Generator Loss: 0.8889
Epoch 1/2... Discriminator Loss: 2.7116... Generator Loss: 0.2134
Epoch 1/2... Discriminator Loss: 1.2404... Generator Loss: 1.1203
Epoch 1/2... Discriminator Loss: 1.9158... Generator Loss: 0.5763
Epoch 1/2... Discriminator Loss: 1.2242... Generator Loss: 1.0521
Epoch 1/2... Discriminator Loss: 0.7315... Generator Loss: 1.8224
Epoch 1/2... Discriminator Loss: 2.2169... Generator Loss: 4.2661
Epoch 1/2... Discriminator Loss: 2.0230... Generator Loss: 0.3908
Epoch 1/2... Discriminator Loss: 2.4600... Generator Loss: 0.2748
Epoch 1/2... Discriminator Loss: 0.8917... Generator Loss: 1.4651
Epoch 1/2... Discriminator Loss: 0.9826... Generator Loss: 1.3093
Epoch 1/2... Discriminator Loss: 1.0489... Generator Loss: 1.0744
Epoch 1/2... Discriminator Loss: 1.7952... Generator Loss: 0.4501
Epoch 1/2... Discriminator Loss: 0.9585... Generator Loss: 1.8951
Epoch 1/2... Discriminator Loss: 2.1017... Generator Loss: 0.3684
Epoch 1/2... Discriminator Loss: 1.3698... Generator Loss: 1.4163
Epoch 1/2... Discriminator Loss: 1.1407... Generator Loss: 2.6431
Epoch 1/2... Discriminator Loss: 1.0938... Generator Loss: 1.3885
Epoch 1/2... Discriminator Loss: 1.7719... Generator Loss: 0.5136
Epoch 1/2... Discriminator Loss: 1.0142... Generator Loss: 0.9150
Epoch 1/2... Discriminator Loss: 1.2657... Generator Loss: 1.0447
Epoch 1/2... Discriminator Loss: 0.8185... Generator Loss: 1.3523
Epoch 1/2... Discriminator Loss: 1.2401... Generator Loss: 0.9386
Epoch 1/2... Discriminator Loss: 1.1092... Generator Loss: 2.4481
Epoch 1/2... Discriminator Loss: 0.9444... Generator Loss: 1.2176
Epoch 1/2... Discriminator Loss: 1.8234... Generator Loss: 0.5686
Epoch 1/2... Discriminator Loss: 0.9733... Generator Loss: 1.6933
Epoch 1/2... Discriminator Loss: 1.1665... Generator Loss: 0.7649
Epoch 1/2... Discriminator Loss: 1.5059... Generator Loss: 2.9724
Epoch 1/2... Discriminator Loss: 1.0843... Generator Loss: 0.9441
Epoch 1/2... Discriminator Loss: 1.3205... Generator Loss: 0.8846
Epoch 1/2... Discriminator Loss: 2.2879... Generator Loss: 0.2671
Epoch 1/2... Discriminator Loss: 1.3081... Generator Loss: 2.1997
Epoch 1/2... Discriminator Loss: 1.5073... Generator Loss: 0.7524
Epoch 1/2... Discriminator Loss: 2.7562... Generator Loss: 0.1634
Epoch 1/2... Discriminator Loss: 1.9940... Generator Loss: 0.3883
Epoch 1/2... Discriminator Loss: 1.8524... Generator Loss: 0.4331
Epoch 1/2... Discriminator Loss: 0.9784... Generator Loss: 0.9976
Epoch 1/2... Discriminator Loss: 1.4662... Generator Loss: 0.5722
Epoch 1/2... Discriminator Loss: 1.0619... Generator Loss: 0.9389
Epoch 1/2... Discriminator Loss: 2.2333... Generator Loss: 3.8509
Epoch 1/2... Discriminator Loss: 1.0236... Generator Loss: 1.0731
Epoch 1/2... Discriminator Loss: 0.9225... Generator Loss: 1.9282
Epoch 1/2... Discriminator Loss: 1.2068... Generator Loss: 1.0511
Epoch 1/2... Discriminator Loss: 1.3326... Generator Loss: 0.7818
Epoch 1/2... Discriminator Loss: 1.6769... Generator Loss: 0.5393
Epoch 1/2... Discriminator Loss: 1.3764... Generator Loss: 1.6047
Epoch 1/2... Discriminator Loss: 1.9873... Generator Loss: 0.4386
Epoch 1/2... Discriminator Loss: 1.2039... Generator Loss: 0.8409
Epoch 1/2... Discriminator Loss: 0.9942... Generator Loss: 1.3815
Epoch 1/2... Discriminator Loss: 1.2518... Generator Loss: 1.5353
Epoch 1/2... Discriminator Loss: 2.4501... Generator Loss: 0.3823
Epoch 1/2... Discriminator Loss: 2.2113... Generator Loss: 0.3064
Epoch 1/2... Discriminator Loss: 0.9939... Generator Loss: 1.6240
Epoch 1/2... Discriminator Loss: 1.1420... Generator Loss: 1.0913
Epoch 1/2... Discriminator Loss: 0.8451... Generator Loss: 1.3837
Epoch 1/2... Discriminator Loss: 1.4407... Generator Loss: 0.5934
Epoch 1/2... Discriminator Loss: 2.1331... Generator Loss: 0.2668
Epoch 1/2... Discriminator Loss: 1.6044... Generator Loss: 0.9944
Epoch 1/2... Discriminator Loss: 0.8468... Generator Loss: 1.3305
Epoch 1/2... Discriminator Loss: 1.1572... Generator Loss: 1.1818
Epoch 1/2... Discriminator Loss: 1.1161... Generator Loss: 0.9127
Epoch 1/2... Discriminator Loss: 1.3017... Generator Loss: 0.9660
Epoch 1/2... Discriminator Loss: 2.7518... Generator Loss: 0.1398
Epoch 1/2... Discriminator Loss: 1.0333... Generator Loss: 2.5645
Epoch 1/2... Discriminator Loss: 0.9235... Generator Loss: 1.1216
Epoch 1/2... Discriminator Loss: 1.2389... Generator Loss: 1.0805
Epoch 1/2... Discriminator Loss: 1.2043... Generator Loss: 0.9151
Epoch 1/2... Discriminator Loss: 0.6883... Generator Loss: 1.7056
Epoch 1/2... Discriminator Loss: 2.8708... Generator Loss: 0.2456
Epoch 1/2... Discriminator Loss: 1.5569... Generator Loss: 0.6046
Epoch 1/2... Discriminator Loss: 1.0614... Generator Loss: 1.1301
Epoch 1/2... Discriminator Loss: 0.9962... Generator Loss: 1.0716
Epoch 1/2... Discriminator Loss: 1.1135... Generator Loss: 0.8899
Epoch 1/2... Discriminator Loss: 1.6662... Generator Loss: 0.4101
Epoch 1/2... Discriminator Loss: 1.6407... Generator Loss: 0.5262
Epoch 1/2... Discriminator Loss: 1.1366... Generator Loss: 2.0198
Epoch 1/2... Discriminator Loss: 1.0608... Generator Loss: 0.8805
Epoch 1/2... Discriminator Loss: 2.1297... Generator Loss: 0.2689
Epoch 1/2... Discriminator Loss: 1.5057... Generator Loss: 0.7819
Epoch 1/2... Discriminator Loss: 1.1085... Generator Loss: 1.5341
Epoch 2/2... Discriminator Loss: 0.7864... Generator Loss: 1.5019
Epoch 2/2... Discriminator Loss: 1.2873... Generator Loss: 0.8066
Epoch 2/2... Discriminator Loss: 1.3851... Generator Loss: 0.6237
Epoch 2/2... Discriminator Loss: 2.0967... Generator Loss: 0.3378
Epoch 2/2... Discriminator Loss: 1.9815... Generator Loss: 0.3407
Epoch 2/2... Discriminator Loss: 1.3600... Generator Loss: 0.8076
Epoch 2/2... Discriminator Loss: 1.2141... Generator Loss: 0.8674
Epoch 2/2... Discriminator Loss: 0.8946... Generator Loss: 1.4386
Epoch 2/2... Discriminator Loss: 1.1880... Generator Loss: 0.8897
Epoch 2/2... Discriminator Loss: 1.9640... Generator Loss: 0.6943
Epoch 2/2... Discriminator Loss: 1.2583... Generator Loss: 0.7801
Epoch 2/2... Discriminator Loss: 0.6908... Generator Loss: 1.6959
Epoch 2/2... Discriminator Loss: 1.4660... Generator Loss: 0.7762
Epoch 2/2... Discriminator Loss: 1.2232... Generator Loss: 0.8231
Epoch 2/2... Discriminator Loss: 0.6690... Generator Loss: 1.8075
Epoch 2/2... Discriminator Loss: 1.0035... Generator Loss: 1.0736
Epoch 2/2... Discriminator Loss: 3.6518... Generator Loss: 0.0870
Epoch 2/2... Discriminator Loss: 1.1137... Generator Loss: 0.8457
Epoch 2/2... Discriminator Loss: 0.8527... Generator Loss: 1.1934
Epoch 2/2... Discriminator Loss: 0.7700... Generator Loss: 1.6414
Epoch 2/2... Discriminator Loss: 1.0191... Generator Loss: 1.0694
Epoch 2/2... Discriminator Loss: 0.8651... Generator Loss: 2.7596
Epoch 2/2... Discriminator Loss: 1.3667... Generator Loss: 0.6483
Epoch 2/2... Discriminator Loss: 1.7026... Generator Loss: 0.6352
Epoch 2/2... Discriminator Loss: 0.7438... Generator Loss: 1.6119
Epoch 2/2... Discriminator Loss: 0.9193... Generator Loss: 1.2199
Epoch 2/2... Discriminator Loss: 1.0263... Generator Loss: 1.3241
Epoch 2/2... Discriminator Loss: 1.7465... Generator Loss: 1.6456
Epoch 2/2... Discriminator Loss: 1.0785... Generator Loss: 0.8683
Epoch 2/2... Discriminator Loss: 1.5979... Generator Loss: 0.5449
Epoch 2/2... Discriminator Loss: 1.4243... Generator Loss: 0.6683
Epoch 2/2... Discriminator Loss: 2.0033... Generator Loss: 0.3602
Epoch 2/2... Discriminator Loss: 1.6344... Generator Loss: 0.4635
Epoch 2/2... Discriminator Loss: 1.3552... Generator Loss: 0.6993
Epoch 2/2... Discriminator Loss: 1.2247... Generator Loss: 0.8258
Epoch 2/2... Discriminator Loss: 1.5537... Generator Loss: 0.6803
Epoch 2/2... Discriminator Loss: 0.9067... Generator Loss: 1.4142
Epoch 2/2... Discriminator Loss: 1.3140... Generator Loss: 0.7438
Epoch 2/2... Discriminator Loss: 1.6025... Generator Loss: 0.4525
Epoch 2/2... Discriminator Loss: 0.7105... Generator Loss: 2.0319
Epoch 2/2... Discriminator Loss: 2.1456... Generator Loss: 0.3011
Epoch 2/2... Discriminator Loss: 0.8223... Generator Loss: 1.4500
Epoch 2/2... Discriminator Loss: 0.5128... Generator Loss: 2.8697
Epoch 2/2... Discriminator Loss: 1.0604... Generator Loss: 1.0173
Epoch 2/2... Discriminator Loss: 0.7837... Generator Loss: 1.5118
Epoch 2/2... Discriminator Loss: 0.6481... Generator Loss: 2.1668
Epoch 2/2... Discriminator Loss: 0.8084... Generator Loss: 1.5661
Epoch 2/2... Discriminator Loss: 0.8723... Generator Loss: 2.5271
Epoch 2/2... Discriminator Loss: 0.6217... Generator Loss: 2.0111
Epoch 2/2... Discriminator Loss: 0.7365... Generator Loss: 1.5930
Epoch 2/2... Discriminator Loss: 2.2788... Generator Loss: 0.3132
Epoch 2/2... Discriminator Loss: 0.6122... Generator Loss: 1.9113
Epoch 2/2... Discriminator Loss: 0.9071... Generator Loss: 1.2045
Epoch 2/2... Discriminator Loss: 0.7213... Generator Loss: 2.1636
Epoch 2/2... Discriminator Loss: 0.9540... Generator Loss: 1.4958
Epoch 2/2... Discriminator Loss: 0.8683... Generator Loss: 1.7351
Epoch 2/2... Discriminator Loss: 0.6842... Generator Loss: 1.6506
Epoch 2/2... Discriminator Loss: 0.9651... Generator Loss: 1.1622
Epoch 2/2... Discriminator Loss: 1.0248... Generator Loss: 2.5601
Epoch 2/2... Discriminator Loss: 1.2872... Generator Loss: 0.6227
Epoch 2/2... Discriminator Loss: 1.2501... Generator Loss: 0.8573
Epoch 2/2... Discriminator Loss: 1.8498... Generator Loss: 0.6923
Epoch 2/2... Discriminator Loss: 0.8709... Generator Loss: 1.7727
Epoch 2/2... Discriminator Loss: 0.7814... Generator Loss: 1.6627
Epoch 2/2... Discriminator Loss: 0.9528... Generator Loss: 1.6834
Epoch 2/2... Discriminator Loss: 0.7995... Generator Loss: 1.6195
Epoch 2/2... Discriminator Loss: 3.6014... Generator Loss: 0.1121
Epoch 2/2... Discriminator Loss: 1.8994... Generator Loss: 0.3768
Epoch 2/2... Discriminator Loss: 1.2504... Generator Loss: 1.0090
Epoch 2/2... Discriminator Loss: 0.7890... Generator Loss: 1.5937
Epoch 2/2... Discriminator Loss: 0.7882... Generator Loss: 1.3483
Epoch 2/2... Discriminator Loss: 2.0840... Generator Loss: 0.5254
Epoch 2/2... Discriminator Loss: 0.6934... Generator Loss: 1.7787
Epoch 2/2... Discriminator Loss: 0.5354... Generator Loss: 2.4948
Epoch 2/2... Discriminator Loss: 1.5519... Generator Loss: 0.9933
Epoch 2/2... Discriminator Loss: 0.9479... Generator Loss: 1.2160
Epoch 2/2... Discriminator Loss: 0.9579... Generator Loss: 1.9417
Epoch 2/2... Discriminator Loss: 1.7073... Generator Loss: 0.6241
Epoch 2/2... Discriminator Loss: 1.3107... Generator Loss: 0.5932
Epoch 2/2... Discriminator Loss: 0.9191... Generator Loss: 1.4294
Epoch 2/2... Discriminator Loss: 1.0001... Generator Loss: 0.9872
Epoch 2/2... Discriminator Loss: 2.4138... Generator Loss: 0.3391
Epoch 2/2... Discriminator Loss: 1.7473... Generator Loss: 1.1731
Epoch 2/2... Discriminator Loss: 1.2238... Generator Loss: 0.7429
Epoch 2/2... Discriminator Loss: 0.8926... Generator Loss: 1.3271
Epoch 2/2... Discriminator Loss: 1.8943... Generator Loss: 0.4150
Epoch 2/2... Discriminator Loss: 1.0814... Generator Loss: 0.9735
Epoch 2/2... Discriminator Loss: 1.2578... Generator Loss: 0.7545
Epoch 2/2... Discriminator Loss: 1.1418... Generator Loss: 4.3982
Epoch 2/2... Discriminator Loss: 1.2795... Generator Loss: 5.3618
Epoch 2/2... Discriminator Loss: 1.0594... Generator Loss: 1.5075
Epoch 2/2... Discriminator Loss: 1.0330... Generator Loss: 1.3884
Epoch 2/2... Discriminator Loss: 0.6821... Generator Loss: 2.0418
Epoch 2/2... Discriminator Loss: 0.6856... Generator Loss: 1.9799
Epoch 2/2... Discriminator Loss: 0.9726... Generator Loss: 1.2402
Epoch 2/2... Discriminator Loss: 0.7840... Generator Loss: 2.1464
Epoch 2/2... Discriminator Loss: 0.9157... Generator Loss: 1.0959
Epoch 2/2... Discriminator Loss: 0.8443... Generator Loss: 2.9287
Epoch 2/2... Discriminator Loss: 1.4479... Generator Loss: 0.6507
Epoch 2/2... Discriminator Loss: 0.8649... Generator Loss: 1.2677
Epoch 2/2... Discriminator Loss: 0.7073... Generator Loss: 1.7199
Epoch 2/2... Discriminator Loss: 1.7508... Generator Loss: 0.4752
Epoch 2/2... Discriminator Loss: 0.8681... Generator Loss: 1.4199
Epoch 2/2... Discriminator Loss: 0.6906... Generator Loss: 2.0806
Epoch 2/2... Discriminator Loss: 0.7333... Generator Loss: 1.9495
Epoch 2/2... Discriminator Loss: 0.7056... Generator Loss: 2.2337
Epoch 2/2... Discriminator Loss: 0.9947... Generator Loss: 0.9077
Epoch 2/2... Discriminator Loss: 0.8592... Generator Loss: 1.3400
Epoch 2/2... Discriminator Loss: 1.0741... Generator Loss: 0.8783
Epoch 2/2... Discriminator Loss: 1.3304... Generator Loss: 1.6097
Epoch 2/2... Discriminator Loss: 2.7587... Generator Loss: 0.3340
Epoch 2/2... Discriminator Loss: 1.6920... Generator Loss: 0.4598
Epoch 2/2... Discriminator Loss: 2.0280... Generator Loss: 0.4397
Epoch 2/2... Discriminator Loss: 1.6617... Generator Loss: 0.7838
Epoch 2/2... Discriminator Loss: 0.9595... Generator Loss: 1.2944
Epoch 2/2... Discriminator Loss: 0.7535... Generator Loss: 1.6208
Epoch 2/2... Discriminator Loss: 1.1253... Generator Loss: 0.9328
Epoch 2/2... Discriminator Loss: 1.2609... Generator Loss: 0.8202
Epoch 2/2... Discriminator Loss: 0.7842... Generator Loss: 1.1728
Epoch 2/2... Discriminator Loss: 1.8111... Generator Loss: 0.5507
Epoch 2/2... Discriminator Loss: 1.2022... Generator Loss: 0.9162
Epoch 2/2... Discriminator Loss: 1.3777... Generator Loss: 0.7312
Epoch 2/2... Discriminator Loss: 2.0066... Generator Loss: 0.3610
Epoch 2/2... Discriminator Loss: 1.0239... Generator Loss: 1.0230
Epoch 2/2... Discriminator Loss: 0.8331... Generator Loss: 1.2929
Epoch 2/2... Discriminator Loss: 1.1367... Generator Loss: 0.8413
Epoch 2/2... Discriminator Loss: 1.0551... Generator Loss: 1.1871
Epoch 2/2... Discriminator Loss: 0.9079... Generator Loss: 1.1972
Epoch 2/2... Discriminator Loss: 0.8940... Generator Loss: 1.3670
Epoch 2/2... Discriminator Loss: 1.4133... Generator Loss: 0.6739
Epoch 2/2... Discriminator Loss: 0.9936... Generator Loss: 1.4391
Epoch 2/2... Discriminator Loss: 1.0105... Generator Loss: 1.1684
Epoch 2/2... Discriminator Loss: 0.7182... Generator Loss: 1.7120
Epoch 2/2... Discriminator Loss: 1.0540... Generator Loss: 0.8815
Epoch 2/2... Discriminator Loss: 0.9519... Generator Loss: 2.5508
Epoch 2/2... Discriminator Loss: 1.9266... Generator Loss: 0.5015
Epoch 2/2... Discriminator Loss: 0.7506... Generator Loss: 1.4245
Epoch 2/2... Discriminator Loss: 1.3694... Generator Loss: 0.6929
Epoch 2/2... Discriminator Loss: 1.4992... Generator Loss: 0.6034
Epoch 2/2... Discriminator Loss: 0.6921... Generator Loss: 1.6221
Epoch 2/2... Discriminator Loss: 0.9542... Generator Loss: 1.0506
Epoch 2/2... Discriminator Loss: 1.9844... Generator Loss: 4.1257
Epoch 2/2... Discriminator Loss: 2.3255... Generator Loss: 0.2383
Epoch 2/2... Discriminator Loss: 1.0388... Generator Loss: 1.0614
Epoch 2/2... Discriminator Loss: 1.4813... Generator Loss: 0.6151
Epoch 2/2... Discriminator Loss: 0.7182... Generator Loss: 1.9203
Epoch 2/2... Discriminator Loss: 1.1163... Generator Loss: 0.8668
Epoch 2/2... Discriminator Loss: 0.8666... Generator Loss: 1.4389
Epoch 2/2... Discriminator Loss: 0.5189... Generator Loss: 2.5324
Epoch 2/2... Discriminator Loss: 1.5781... Generator Loss: 0.6679
Epoch 2/2... Discriminator Loss: 1.1545... Generator Loss: 2.7751
Epoch 2/2... Discriminator Loss: 0.9529... Generator Loss: 1.1210
Epoch 2/2... Discriminator Loss: 0.7200... Generator Loss: 1.5462
Epoch 2/2... Discriminator Loss: 0.8421... Generator Loss: 1.5183
Epoch 2/2... Discriminator Loss: 1.4447... Generator Loss: 0.7708
Epoch 2/2... Discriminator Loss: 0.8379... Generator Loss: 1.2729
Epoch 2/2... Discriminator Loss: 0.7481... Generator Loss: 1.9094
Epoch 2/2... Discriminator Loss: 1.0244... Generator Loss: 1.4291
Epoch 2/2... Discriminator Loss: 1.6972... Generator Loss: 0.5885
Epoch 2/2... Discriminator Loss: 3.0959... Generator Loss: 0.1539
Epoch 2/2... Discriminator Loss: 2.5431... Generator Loss: 4.8422
Epoch 2/2... Discriminator Loss: 0.9853... Generator Loss: 2.4585
Epoch 2/2... Discriminator Loss: 0.7055... Generator Loss: 1.5730
Epoch 2/2... Discriminator Loss: 0.8752... Generator Loss: 1.9007
Epoch 2/2... Discriminator Loss: 0.9167... Generator Loss: 1.2206
Epoch 2/2... Discriminator Loss: 0.9112... Generator Loss: 1.2477
Epoch 2/2... Discriminator Loss: 1.3533... Generator Loss: 0.7418
Epoch 2/2... Discriminator Loss: 0.7250... Generator Loss: 1.5776
Epoch 2/2... Discriminator Loss: 0.8002... Generator Loss: 3.4462
Epoch 2/2... Discriminator Loss: 1.7920... Generator Loss: 0.5479
Epoch 2/2... Discriminator Loss: 1.7037... Generator Loss: 0.4592
Epoch 2/2... Discriminator Loss: 1.3108... Generator Loss: 1.0704
Epoch 2/2... Discriminator Loss: 1.0224... Generator Loss: 1.1180
Epoch 2/2... Discriminator Loss: 0.6873... Generator Loss: 2.0704
Epoch 2/2... Discriminator Loss: 1.2652... Generator Loss: 1.9002
Epoch 2/2... Discriminator Loss: 1.0057... Generator Loss: 1.0204
Epoch 2/2... Discriminator Loss: 1.4294... Generator Loss: 0.6457
Epoch 2/2... Discriminator Loss: 0.6655... Generator Loss: 2.6429
Epoch 2/2... Discriminator Loss: 0.9645... Generator Loss: 1.0879
Epoch 2/2... Discriminator Loss: 0.7680... Generator Loss: 3.0487
Epoch 2/2... Discriminator Loss: 1.5097... Generator Loss: 0.6106
Epoch 2/2... Discriminator Loss: 1.1032... Generator Loss: 1.1521
Epoch 2/2... Discriminator Loss: 0.7121... Generator Loss: 1.8069
Epoch 2/2... Discriminator Loss: 0.9280... Generator Loss: 1.0576
Epoch 2/2... Discriminator Loss: 0.7861... Generator Loss: 2.4726
Epoch 2/2... Discriminator Loss: 1.4342... Generator Loss: 0.5931
Epoch 2/2... Discriminator Loss: 0.8203... Generator Loss: 1.9958
Epoch 2/2... Discriminator Loss: 1.1706... Generator Loss: 4.3603

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 4.2353... Generator Loss: 1.0928
Epoch 1/1... Discriminator Loss: 2.6069... Generator Loss: 1.1657
Epoch 1/1... Discriminator Loss: 1.7437... Generator Loss: 0.6422
Epoch 1/1... Discriminator Loss: 5.1119... Generator Loss: 0.0544
Epoch 1/1... Discriminator Loss: 0.7068... Generator Loss: 3.7102
Epoch 1/1... Discriminator Loss: 0.6116... Generator Loss: 3.0479
Epoch 1/1... Discriminator Loss: 1.1222... Generator Loss: 3.3141
Epoch 1/1... Discriminator Loss: 1.6669... Generator Loss: 4.2464
Epoch 1/1... Discriminator Loss: 2.6037... Generator Loss: 2.3480
Epoch 1/1... Discriminator Loss: 0.9742... Generator Loss: 1.6773
Epoch 1/1... Discriminator Loss: 1.5597... Generator Loss: 1.0923
Epoch 1/1... Discriminator Loss: 1.1074... Generator Loss: 1.2099
Epoch 1/1... Discriminator Loss: 3.7854... Generator Loss: 3.6852
Epoch 1/1... Discriminator Loss: 1.9533... Generator Loss: 3.9980
Epoch 1/1... Discriminator Loss: 2.7168... Generator Loss: 1.3193
Epoch 1/1... Discriminator Loss: 1.5389... Generator Loss: 2.3909
Epoch 1/1... Discriminator Loss: 1.1304... Generator Loss: 1.0366
Epoch 1/1... Discriminator Loss: 1.3405... Generator Loss: 1.3640
Epoch 1/1... Discriminator Loss: 1.6885... Generator Loss: 2.1868
Epoch 1/1... Discriminator Loss: 0.5856... Generator Loss: 2.7022
Epoch 1/1... Discriminator Loss: 2.8559... Generator Loss: 0.8870
Epoch 1/1... Discriminator Loss: 3.4760... Generator Loss: 3.5823
Epoch 1/1... Discriminator Loss: 1.5817... Generator Loss: 1.0852
Epoch 1/1... Discriminator Loss: 2.1818... Generator Loss: 0.3624
Epoch 1/1... Discriminator Loss: 1.6197... Generator Loss: 2.2053
Epoch 1/1... Discriminator Loss: 1.6638... Generator Loss: 3.5565
Epoch 1/1... Discriminator Loss: 1.5251... Generator Loss: 1.4684
Epoch 1/1... Discriminator Loss: 2.1088... Generator Loss: 0.4169
Epoch 1/1... Discriminator Loss: 1.3758... Generator Loss: 1.4182
Epoch 1/1... Discriminator Loss: 3.2727... Generator Loss: 0.6113
Epoch 1/1... Discriminator Loss: 1.3939... Generator Loss: 0.6237
Epoch 1/1... Discriminator Loss: 0.9540... Generator Loss: 2.2856
Epoch 1/1... Discriminator Loss: 1.6232... Generator Loss: 1.8568
Epoch 1/1... Discriminator Loss: 1.3007... Generator Loss: 1.3427
Epoch 1/1... Discriminator Loss: 1.6626... Generator Loss: 1.5692
Epoch 1/1... Discriminator Loss: 1.0972... Generator Loss: 1.0118
Epoch 1/1... Discriminator Loss: 1.0734... Generator Loss: 1.3082
Epoch 1/1... Discriminator Loss: 1.9116... Generator Loss: 4.0501
Epoch 1/1... Discriminator Loss: 1.0076... Generator Loss: 1.2357
Epoch 1/1... Discriminator Loss: 0.7552... Generator Loss: 1.5155
Epoch 1/1... Discriminator Loss: 0.8149... Generator Loss: 1.1583
Epoch 1/1... Discriminator Loss: 1.6350... Generator Loss: 0.5442
Epoch 1/1... Discriminator Loss: 0.8236... Generator Loss: 1.2124
Epoch 1/1... Discriminator Loss: 1.0742... Generator Loss: 1.2872
Epoch 1/1... Discriminator Loss: 1.4420... Generator Loss: 0.6921
Epoch 1/1... Discriminator Loss: 0.7537... Generator Loss: 1.3864
Epoch 1/1... Discriminator Loss: 1.4899... Generator Loss: 0.6319
Epoch 1/1... Discriminator Loss: 1.5044... Generator Loss: 0.9458
Epoch 1/1... Discriminator Loss: 1.4289... Generator Loss: 0.7131
Epoch 1/1... Discriminator Loss: 0.7180... Generator Loss: 2.2895
Epoch 1/1... Discriminator Loss: 0.9147... Generator Loss: 1.0166
Epoch 1/1... Discriminator Loss: 0.7350... Generator Loss: 1.8548
Epoch 1/1... Discriminator Loss: 0.9220... Generator Loss: 1.3568
Epoch 1/1... Discriminator Loss: 0.9158... Generator Loss: 1.0566
Epoch 1/1... Discriminator Loss: 0.6306... Generator Loss: 1.7138
Epoch 1/1... Discriminator Loss: 0.9002... Generator Loss: 1.4653
Epoch 1/1... Discriminator Loss: 1.5356... Generator Loss: 2.8338
Epoch 1/1... Discriminator Loss: 0.8897... Generator Loss: 0.9922
Epoch 1/1... Discriminator Loss: 0.8934... Generator Loss: 1.5296
Epoch 1/1... Discriminator Loss: 1.2554... Generator Loss: 0.6943
Epoch 1/1... Discriminator Loss: 0.4663... Generator Loss: 3.7849
Epoch 1/1... Discriminator Loss: 0.6863... Generator Loss: 2.3199
Epoch 1/1... Discriminator Loss: 0.9792... Generator Loss: 1.0609
Epoch 1/1... Discriminator Loss: 0.8813... Generator Loss: 2.3580
Epoch 1/1... Discriminator Loss: 0.9893... Generator Loss: 1.4705
Epoch 1/1... Discriminator Loss: 1.1379... Generator Loss: 0.8043
Epoch 1/1... Discriminator Loss: 3.5355... Generator Loss: 4.2446
Epoch 1/1... Discriminator Loss: 2.8854... Generator Loss: 0.3680
Epoch 1/1... Discriminator Loss: 0.9516... Generator Loss: 1.1291
Epoch 1/1... Discriminator Loss: 1.7226... Generator Loss: 0.4666
Epoch 1/1... Discriminator Loss: 1.9347... Generator Loss: 0.3041
Epoch 1/1... Discriminator Loss: 1.2400... Generator Loss: 1.2577
Epoch 1/1... Discriminator Loss: 1.0712... Generator Loss: 0.8283
Epoch 1/1... Discriminator Loss: 1.1230... Generator Loss: 1.0471
Epoch 1/1... Discriminator Loss: 0.5378... Generator Loss: 2.4445
Epoch 1/1... Discriminator Loss: 0.8331... Generator Loss: 1.7669
Epoch 1/1... Discriminator Loss: 1.4972... Generator Loss: 2.6231
Epoch 1/1... Discriminator Loss: 1.1016... Generator Loss: 2.0403
Epoch 1/1... Discriminator Loss: 0.8192... Generator Loss: 1.2541
Epoch 1/1... Discriminator Loss: 0.5133... Generator Loss: 2.8299
Epoch 1/1... Discriminator Loss: 1.0308... Generator Loss: 1.0659
Epoch 1/1... Discriminator Loss: 1.0528... Generator Loss: 1.2989
Epoch 1/1... Discriminator Loss: 0.6640... Generator Loss: 2.1443
Epoch 1/1... Discriminator Loss: 0.5274... Generator Loss: 2.7574
Epoch 1/1... Discriminator Loss: 1.3026... Generator Loss: 0.9578
Epoch 1/1... Discriminator Loss: 1.8510... Generator Loss: 0.8639
Epoch 1/1... Discriminator Loss: 1.2835... Generator Loss: 1.4976
Epoch 1/1... Discriminator Loss: 1.7047... Generator Loss: 0.3677
Epoch 1/1... Discriminator Loss: 0.9061... Generator Loss: 0.9529
Epoch 1/1... Discriminator Loss: 1.5113... Generator Loss: 0.4524
Epoch 1/1... Discriminator Loss: 1.2627... Generator Loss: 0.9547
Epoch 1/1... Discriminator Loss: 1.4369... Generator Loss: 2.2821
Epoch 1/1... Discriminator Loss: 1.7997... Generator Loss: 0.3407
Epoch 1/1... Discriminator Loss: 1.3536... Generator Loss: 1.4279
Epoch 1/1... Discriminator Loss: 1.4165... Generator Loss: 0.8578
Epoch 1/1... Discriminator Loss: 1.0673... Generator Loss: 1.5253
Epoch 1/1... Discriminator Loss: 1.5383... Generator Loss: 0.4109
Epoch 1/1... Discriminator Loss: 1.5293... Generator Loss: 0.6985
Epoch 1/1... Discriminator Loss: 1.3354... Generator Loss: 0.9461
Epoch 1/1... Discriminator Loss: 1.2411... Generator Loss: 0.6839
Epoch 1/1... Discriminator Loss: 0.9181... Generator Loss: 0.9131
Epoch 1/1... Discriminator Loss: 0.7862... Generator Loss: 1.6658
Epoch 1/1... Discriminator Loss: 0.8102... Generator Loss: 1.3663
Epoch 1/1... Discriminator Loss: 1.4941... Generator Loss: 0.6739
Epoch 1/1... Discriminator Loss: 1.3930... Generator Loss: 0.6864
Epoch 1/1... Discriminator Loss: 1.0738... Generator Loss: 0.7876
Epoch 1/1... Discriminator Loss: 1.3233... Generator Loss: 1.2422
Epoch 1/1... Discriminator Loss: 1.2112... Generator Loss: 0.7818
Epoch 1/1... Discriminator Loss: 1.1010... Generator Loss: 0.9663
Epoch 1/1... Discriminator Loss: 1.0965... Generator Loss: 1.2194
Epoch 1/1... Discriminator Loss: 1.1813... Generator Loss: 1.5104
Epoch 1/1... Discriminator Loss: 1.2349... Generator Loss: 0.9825
Epoch 1/1... Discriminator Loss: 1.0298... Generator Loss: 1.0013
Epoch 1/1... Discriminator Loss: 1.3024... Generator Loss: 1.4968
Epoch 1/1... Discriminator Loss: 1.7145... Generator Loss: 1.8119
Epoch 1/1... Discriminator Loss: 1.2180... Generator Loss: 0.9178
Epoch 1/1... Discriminator Loss: 1.3284... Generator Loss: 0.8601
Epoch 1/1... Discriminator Loss: 1.3859... Generator Loss: 1.1917
Epoch 1/1... Discriminator Loss: 1.4494... Generator Loss: 0.8991
Epoch 1/1... Discriminator Loss: 1.0237... Generator Loss: 1.2074
Epoch 1/1... Discriminator Loss: 1.3844... Generator Loss: 0.7336
Epoch 1/1... Discriminator Loss: 1.4354... Generator Loss: 0.5180
Epoch 1/1... Discriminator Loss: 1.5170... Generator Loss: 0.9053
Epoch 1/1... Discriminator Loss: 1.1468... Generator Loss: 0.9250
Epoch 1/1... Discriminator Loss: 1.5287... Generator Loss: 1.1127
Epoch 1/1... Discriminator Loss: 1.2736... Generator Loss: 0.7446
Epoch 1/1... Discriminator Loss: 1.1278... Generator Loss: 1.0769
Epoch 1/1... Discriminator Loss: 0.8306... Generator Loss: 1.4498
Epoch 1/1... Discriminator Loss: 1.6773... Generator Loss: 0.6587
Epoch 1/1... Discriminator Loss: 1.1337... Generator Loss: 0.7970
Epoch 1/1... Discriminator Loss: 1.0378... Generator Loss: 0.8630
Epoch 1/1... Discriminator Loss: 1.2358... Generator Loss: 1.7397
Epoch 1/1... Discriminator Loss: 0.9201... Generator Loss: 1.3223
Epoch 1/1... Discriminator Loss: 1.5034... Generator Loss: 0.5160
Epoch 1/1... Discriminator Loss: 1.1416... Generator Loss: 1.2796
Epoch 1/1... Discriminator Loss: 0.9754... Generator Loss: 1.0876
Epoch 1/1... Discriminator Loss: 1.2466... Generator Loss: 0.9543
Epoch 1/1... Discriminator Loss: 2.1198... Generator Loss: 0.3785
Epoch 1/1... Discriminator Loss: 1.0912... Generator Loss: 0.8912
Epoch 1/1... Discriminator Loss: 0.9050... Generator Loss: 1.1988
Epoch 1/1... Discriminator Loss: 1.5290... Generator Loss: 1.9901
Epoch 1/1... Discriminator Loss: 1.1191... Generator Loss: 0.9217
Epoch 1/1... Discriminator Loss: 1.1165... Generator Loss: 1.7482
Epoch 1/1... Discriminator Loss: 1.1744... Generator Loss: 1.7322
Epoch 1/1... Discriminator Loss: 1.2546... Generator Loss: 1.8707
Epoch 1/1... Discriminator Loss: 0.6335... Generator Loss: 1.9056
Epoch 1/1... Discriminator Loss: 1.5965... Generator Loss: 0.4985
Epoch 1/1... Discriminator Loss: 3.6387... Generator Loss: 3.6677
Epoch 1/1... Discriminator Loss: 0.8335... Generator Loss: 1.3069
Epoch 1/1... Discriminator Loss: 0.9996... Generator Loss: 1.0695
Epoch 1/1... Discriminator Loss: 0.9326... Generator Loss: 1.2368
Epoch 1/1... Discriminator Loss: 0.8789... Generator Loss: 1.5029
Epoch 1/1... Discriminator Loss: 1.1835... Generator Loss: 0.8640
Epoch 1/1... Discriminator Loss: 1.1730... Generator Loss: 2.3267
Epoch 1/1... Discriminator Loss: 0.7766... Generator Loss: 1.1573
Epoch 1/1... Discriminator Loss: 1.3414... Generator Loss: 1.0330
Epoch 1/1... Discriminator Loss: 1.0533... Generator Loss: 1.1207
Epoch 1/1... Discriminator Loss: 1.2735... Generator Loss: 0.8605
Epoch 1/1... Discriminator Loss: 1.0555... Generator Loss: 0.8308
Epoch 1/1... Discriminator Loss: 1.3109... Generator Loss: 0.6494
Epoch 1/1... Discriminator Loss: 0.9523... Generator Loss: 0.9571
Epoch 1/1... Discriminator Loss: 0.9701... Generator Loss: 0.9923
Epoch 1/1... Discriminator Loss: 2.0355... Generator Loss: 2.8879
Epoch 1/1... Discriminator Loss: 1.2381... Generator Loss: 1.5367
Epoch 1/1... Discriminator Loss: 0.7009... Generator Loss: 1.8364
Epoch 1/1... Discriminator Loss: 0.5326... Generator Loss: 1.8483
Epoch 1/1... Discriminator Loss: 1.0448... Generator Loss: 1.7713
Epoch 1/1... Discriminator Loss: 2.0119... Generator Loss: 0.5559
Epoch 1/1... Discriminator Loss: 1.3079... Generator Loss: 0.8752
Epoch 1/1... Discriminator Loss: 0.9173... Generator Loss: 1.1050
Epoch 1/1... Discriminator Loss: 0.9689... Generator Loss: 1.0907
Epoch 1/1... Discriminator Loss: 1.0419... Generator Loss: 1.0807
Epoch 1/1... Discriminator Loss: 1.1004... Generator Loss: 0.7087
Epoch 1/1... Discriminator Loss: 2.9177... Generator Loss: 3.0526
Epoch 1/1... Discriminator Loss: 0.6901... Generator Loss: 1.4390
Epoch 1/1... Discriminator Loss: 0.9597... Generator Loss: 0.8982
Epoch 1/1... Discriminator Loss: 0.6213... Generator Loss: 2.0821
Epoch 1/1... Discriminator Loss: 2.6404... Generator Loss: 3.8222
Epoch 1/1... Discriminator Loss: 0.9735... Generator Loss: 1.1878
Epoch 1/1... Discriminator Loss: 0.9640... Generator Loss: 1.6771
Epoch 1/1... Discriminator Loss: 0.9318... Generator Loss: 1.2816
Epoch 1/1... Discriminator Loss: 0.7398... Generator Loss: 1.4419
Epoch 1/1... Discriminator Loss: 1.0495... Generator Loss: 0.8992
Epoch 1/1... Discriminator Loss: 1.4576... Generator Loss: 0.6364
Epoch 1/1... Discriminator Loss: 1.3653... Generator Loss: 0.8161
Epoch 1/1... Discriminator Loss: 1.1109... Generator Loss: 0.7240
Epoch 1/1... Discriminator Loss: 1.0144... Generator Loss: 1.9440
Epoch 1/1... Discriminator Loss: 1.2266... Generator Loss: 1.1591
Epoch 1/1... Discriminator Loss: 0.7069... Generator Loss: 1.4868
Epoch 1/1... Discriminator Loss: 0.7752... Generator Loss: 1.2082
Epoch 1/1... Discriminator Loss: 1.3166... Generator Loss: 1.1325
Epoch 1/1... Discriminator Loss: 2.3217... Generator Loss: 2.9947
Epoch 1/1... Discriminator Loss: 0.9947... Generator Loss: 1.1801
Epoch 1/1... Discriminator Loss: 1.0634... Generator Loss: 0.9172
Epoch 1/1... Discriminator Loss: 0.9618... Generator Loss: 1.7627
Epoch 1/1... Discriminator Loss: 1.1409... Generator Loss: 1.1509
Epoch 1/1... Discriminator Loss: 0.8863... Generator Loss: 1.1835
Epoch 1/1... Discriminator Loss: 1.1875... Generator Loss: 0.6895
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 0.6302
Epoch 1/1... Discriminator Loss: 0.7186... Generator Loss: 1.5366
Epoch 1/1... Discriminator Loss: 2.2158... Generator Loss: 0.2975
Epoch 1/1... Discriminator Loss: 1.4415... Generator Loss: 0.8617
Epoch 1/1... Discriminator Loss: 1.0773... Generator Loss: 1.3055
Epoch 1/1... Discriminator Loss: 1.3098... Generator Loss: 0.7414
Epoch 1/1... Discriminator Loss: 1.1673... Generator Loss: 1.1861
Epoch 1/1... Discriminator Loss: 1.4971... Generator Loss: 0.6283
Epoch 1/1... Discriminator Loss: 1.0444... Generator Loss: 1.1865
Epoch 1/1... Discriminator Loss: 1.1450... Generator Loss: 0.9246
Epoch 1/1... Discriminator Loss: 1.1522... Generator Loss: 1.2206
Epoch 1/1... Discriminator Loss: 1.0792... Generator Loss: 1.1240
Epoch 1/1... Discriminator Loss: 1.0699... Generator Loss: 0.9702
Epoch 1/1... Discriminator Loss: 1.2476... Generator Loss: 0.7101
Epoch 1/1... Discriminator Loss: 1.3141... Generator Loss: 1.1144
Epoch 1/1... Discriminator Loss: 1.2274... Generator Loss: 1.1615
Epoch 1/1... Discriminator Loss: 1.9159... Generator Loss: 0.3995
Epoch 1/1... Discriminator Loss: 1.4891... Generator Loss: 1.1899
Epoch 1/1... Discriminator Loss: 1.4283... Generator Loss: 0.4891
Epoch 1/1... Discriminator Loss: 1.3103... Generator Loss: 0.8176
Epoch 1/1... Discriminator Loss: 1.0162... Generator Loss: 1.3630
Epoch 1/1... Discriminator Loss: 1.4422... Generator Loss: 0.5931
Epoch 1/1... Discriminator Loss: 0.8237... Generator Loss: 1.1317
Epoch 1/1... Discriminator Loss: 0.9841... Generator Loss: 1.6018
Epoch 1/1... Discriminator Loss: 1.1833... Generator Loss: 0.6987
Epoch 1/1... Discriminator Loss: 1.0430... Generator Loss: 1.1505
Epoch 1/1... Discriminator Loss: 1.0857... Generator Loss: 0.8695
Epoch 1/1... Discriminator Loss: 1.7610... Generator Loss: 0.4182
Epoch 1/1... Discriminator Loss: 1.4356... Generator Loss: 0.8100
Epoch 1/1... Discriminator Loss: 0.9307... Generator Loss: 1.5805
Epoch 1/1... Discriminator Loss: 0.5740... Generator Loss: 1.8651
Epoch 1/1... Discriminator Loss: 0.6872... Generator Loss: 1.6294
Epoch 1/1... Discriminator Loss: 1.0457... Generator Loss: 0.9048
Epoch 1/1... Discriminator Loss: 1.3022... Generator Loss: 0.6026
Epoch 1/1... Discriminator Loss: 1.2844... Generator Loss: 2.1333
Epoch 1/1... Discriminator Loss: 0.8168... Generator Loss: 1.1749
Epoch 1/1... Discriminator Loss: 0.9459... Generator Loss: 1.6352
Epoch 1/1... Discriminator Loss: 0.8422... Generator Loss: 2.2319
Epoch 1/1... Discriminator Loss: 0.6388... Generator Loss: 1.6492
Epoch 1/1... Discriminator Loss: 1.0244... Generator Loss: 1.4031
Epoch 1/1... Discriminator Loss: 1.7513... Generator Loss: 0.3927
Epoch 1/1... Discriminator Loss: 1.3580... Generator Loss: 0.9218
Epoch 1/1... Discriminator Loss: 1.2679... Generator Loss: 1.2530
Epoch 1/1... Discriminator Loss: 1.1147... Generator Loss: 0.8057
Epoch 1/1... Discriminator Loss: 0.9157... Generator Loss: 1.3747
Epoch 1/1... Discriminator Loss: 1.3306... Generator Loss: 0.5991
Epoch 1/1... Discriminator Loss: 1.1822... Generator Loss: 0.9448
Epoch 1/1... Discriminator Loss: 1.4633... Generator Loss: 2.0202
Epoch 1/1... Discriminator Loss: 1.0121... Generator Loss: 0.9197
Epoch 1/1... Discriminator Loss: 0.8830... Generator Loss: 1.3214
Epoch 1/1... Discriminator Loss: 1.3464... Generator Loss: 1.4363
Epoch 1/1... Discriminator Loss: 0.9767... Generator Loss: 1.2184
Epoch 1/1... Discriminator Loss: 1.0215... Generator Loss: 0.8561
Epoch 1/1... Discriminator Loss: 0.6831... Generator Loss: 1.6513
Epoch 1/1... Discriminator Loss: 1.9541... Generator Loss: 1.5339
Epoch 1/1... Discriminator Loss: 0.8211... Generator Loss: 1.6635
Epoch 1/1... Discriminator Loss: 1.3776... Generator Loss: 0.6053
Epoch 1/1... Discriminator Loss: 0.7316... Generator Loss: 1.6459
Epoch 1/1... Discriminator Loss: 1.0687... Generator Loss: 3.0206
Epoch 1/1... Discriminator Loss: 1.9241... Generator Loss: 2.4172
Epoch 1/1... Discriminator Loss: 0.6819... Generator Loss: 1.3891
Epoch 1/1... Discriminator Loss: 0.7566... Generator Loss: 1.7525
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 1.5563
Epoch 1/1... Discriminator Loss: 1.6648... Generator Loss: 0.5890
Epoch 1/1... Discriminator Loss: 1.0251... Generator Loss: 1.2444
Epoch 1/1... Discriminator Loss: 2.1566... Generator Loss: 0.2334
Epoch 1/1... Discriminator Loss: 0.4543... Generator Loss: 2.6233
Epoch 1/1... Discriminator Loss: 0.9833... Generator Loss: 1.0411
Epoch 1/1... Discriminator Loss: 1.3954... Generator Loss: 0.6156
Epoch 1/1... Discriminator Loss: 1.2248... Generator Loss: 0.6566
Epoch 1/1... Discriminator Loss: 0.7746... Generator Loss: 1.5303
Epoch 1/1... Discriminator Loss: 0.5133... Generator Loss: 2.3948
Epoch 1/1... Discriminator Loss: 0.9715... Generator Loss: 1.6680
Epoch 1/1... Discriminator Loss: 0.7805... Generator Loss: 2.3436
Epoch 1/1... Discriminator Loss: 1.6923... Generator Loss: 0.3688
Epoch 1/1... Discriminator Loss: 1.5097... Generator Loss: 2.1276
Epoch 1/1... Discriminator Loss: 0.9392... Generator Loss: 1.0236
Epoch 1/1... Discriminator Loss: 0.9986... Generator Loss: 1.2020
Epoch 1/1... Discriminator Loss: 0.7560... Generator Loss: 1.3559
Epoch 1/1... Discriminator Loss: 0.5609... Generator Loss: 2.0938
Epoch 1/1... Discriminator Loss: 0.5571... Generator Loss: 2.1269
Epoch 1/1... Discriminator Loss: 0.9809... Generator Loss: 1.1574
Epoch 1/1... Discriminator Loss: 0.4299... Generator Loss: 3.1751
Epoch 1/1... Discriminator Loss: 0.6220... Generator Loss: 1.6875
Epoch 1/1... Discriminator Loss: 1.2837... Generator Loss: 0.7812
Epoch 1/1... Discriminator Loss: 0.6452... Generator Loss: 1.8195
Epoch 1/1... Discriminator Loss: 0.8593... Generator Loss: 2.5838
Epoch 1/1... Discriminator Loss: 0.5972... Generator Loss: 2.8050
Epoch 1/1... Discriminator Loss: 0.3900... Generator Loss: 4.1631
Epoch 1/1... Discriminator Loss: 1.2319... Generator Loss: 0.7671
Epoch 1/1... Discriminator Loss: 1.1163... Generator Loss: 1.1732
Epoch 1/1... Discriminator Loss: 0.9279... Generator Loss: 1.5569
Epoch 1/1... Discriminator Loss: 0.8600... Generator Loss: 2.0763
Epoch 1/1... Discriminator Loss: 0.9108... Generator Loss: 1.3036
Epoch 1/1... Discriminator Loss: 2.9596... Generator Loss: 3.5293
Epoch 1/1... Discriminator Loss: 0.9442... Generator Loss: 0.9513
Epoch 1/1... Discriminator Loss: 1.1870... Generator Loss: 0.7116
Epoch 1/1... Discriminator Loss: 0.9585... Generator Loss: 1.2724
Epoch 1/1... Discriminator Loss: 0.5385... Generator Loss: 2.2086
Epoch 1/1... Discriminator Loss: 1.3112... Generator Loss: 0.9717
Epoch 1/1... Discriminator Loss: 1.4643... Generator Loss: 0.5818
Epoch 1/1... Discriminator Loss: 0.9847... Generator Loss: 0.9108
Epoch 1/1... Discriminator Loss: 1.0733... Generator Loss: 1.4461
Epoch 1/1... Discriminator Loss: 0.5867... Generator Loss: 1.8957
Epoch 1/1... Discriminator Loss: 0.6695... Generator Loss: 1.5935
Epoch 1/1... Discriminator Loss: 0.5319... Generator Loss: 2.2406
Epoch 1/1... Discriminator Loss: 1.0907... Generator Loss: 1.3190
Epoch 1/1... Discriminator Loss: 0.6113... Generator Loss: 2.7849
Epoch 1/1... Discriminator Loss: 0.6143... Generator Loss: 1.8155
Epoch 1/1... Discriminator Loss: 0.7915... Generator Loss: 1.7317
Epoch 1/1... Discriminator Loss: 0.5794... Generator Loss: 2.4351
Epoch 1/1... Discriminator Loss: 1.1225... Generator Loss: 1.0089
Epoch 1/1... Discriminator Loss: 1.9017... Generator Loss: 2.2280
Epoch 1/1... Discriminator Loss: 0.9007... Generator Loss: 1.0055
Epoch 1/1... Discriminator Loss: 1.5820... Generator Loss: 1.0004
Epoch 1/1... Discriminator Loss: 1.2943... Generator Loss: 1.0072
Epoch 1/1... Discriminator Loss: 1.2757... Generator Loss: 0.7958
Epoch 1/1... Discriminator Loss: 1.0520... Generator Loss: 0.9668
Epoch 1/1... Discriminator Loss: 1.0456... Generator Loss: 1.0571
Epoch 1/1... Discriminator Loss: 1.3209... Generator Loss: 0.9504
Epoch 1/1... Discriminator Loss: 1.0186... Generator Loss: 1.1892
Epoch 1/1... Discriminator Loss: 0.9698... Generator Loss: 1.0983
Epoch 1/1... Discriminator Loss: 1.1386... Generator Loss: 1.3833
Epoch 1/1... Discriminator Loss: 0.9514... Generator Loss: 1.1049
Epoch 1/1... Discriminator Loss: 1.2193... Generator Loss: 1.0625
Epoch 1/1... Discriminator Loss: 0.9017... Generator Loss: 1.0160
Epoch 1/1... Discriminator Loss: 2.4456... Generator Loss: 2.5860

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.